Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Innovative geospatial solutions are necessary to tackle complex environmental challenges. Mobile mapping systems (MMSs) are such key innovations emerging in this effort. MMSs, with a wide range of applications, significantly impact our increasingly developed data collection technologies by enhancing our understanding of the environment, enabling us to create more detailed models of natural resources, and optimizing the way we live on Earth. In this paper, we present and analyze recent advancements in MMS technologies, focusing on computational and modeling aspects, as well as the latest state-of-the-art sensor, hardware, and software developments. Special attention is given to the trends observed over the past decade, supported by a review of foundational literature. Finally, we outline our vision for the future of MMS, offering insights into the potential for further research and the exciting possibilities that lie ahead in this rapidly evolving field of science and technology.